Project summary
Findings:
Twitter data
Twitter data was obtained freely through a partnership between UCSB Library and Crimson Hexagon. Before downloading, the data was queried to meet the following conditions:
Crimson Hexagon only allows 10,000 randomly selected tweets to be exported, manually, at a time in .xls format. Due to this restriction, data was manually downloaded for every 2 days in order to capture all tweets. There were around 5000 average number of daily tweets that met these conditions.
The Crimson Hexagon data did not contain all desired information, including whether or not the tweet was geotagged. To get this information we used the python twarc library to “rehydrate” the data using individual tweet ids and store the tweet information as .json files. From here we were able to remove all tweets that did not have a geotag, giving us a total of 82,876 tweets.
Here is a sample of the type of the final twitter information we obtained.
| created_at | tweet_id | full_text | user_id | user_location | geo_type | geo_coordinates | language | retweet_count | favorite_count | lat | lon |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Thu Mar 19 06:42:44 +0000 2015 | 5.784465e+17 | You don’t become obsessed with money until you get a job | 2381122279 | Oxnard, CA | Point | c(34.46758064, -119.75160822) | en | 0 | 1 | 34.46758 | -119.7516 |
| Sat Oct 28 19:11:57 +0000 2017 | 9.243531e+17 | Adorable! Happy fall from goletafloral who made this for me for the salon. I love surprises… https://t.co/VA82T9erlQ | 204506968 | Santa Barbara, CA | Point | c(34.4348412, -119.8198929) | en | 0 | 0 | 34.43484 | -119.8199 |
| Sun Mar 22 05:54:52 +0000 2015 | 5.795216e+17 | @Thomas_lundyyy true true😂find a new friend | 189565102 | 13/9/13 r.i.p granny ily xxx | Point | c(34.44111088, -119.74887975) | en | 0 | 0 | 34.44111 | -119.7489 |
| Wed Mar 23 00:05:21 +0000 2016 | 7.124300e+17 | Amazing cuisine comes out of @thelarksb kitchen. It’s no wonder with such an amazing kitchen… https://t.co/j0f6KYxJpd | 1368498818 | Santa Barbara | Point | c(34.41492371, -119.69080081) | en | 1 | 2 | 34.41492 | -119.6908 |
| Tue Mar 10 06:04:35 +0000 2015 | 5.751754e+17 | My candles, my jazz, and good conversation ❤️ #goodnight | 39122583 | San Francisco, CA | Point | c(34.41186981, -119.8548513) | en | 0 | 0 | 34.41187 | -119.8549 |
| Mon Nov 30 00:08:51 +0000 2015 | 6.711187e+17 | Someone put some origami cranes on these trees that fell from the cliffs #nomads in #California @… https://t.co/rJZIYZlwAN | 23234672 | Santa Barbara, CA | Point | c(34.40295, -119.7439666) | en | 0 | 0 | 34.40295 | -119.7440 |
| Tue Mar 17 00:29:01 +0000 2015 | 5.776277e+17 | lmao one of my housemates surprised visited her and we had to hide the all our bongs and hookah asap 😂 | 422088295 | in my bag | Point | c(34.41379288, -119.86042301) | en | 0 | 0 | 34.41379 | -119.8604 |
| Thu Sep 01 19:33:07 +0000 2016 | 7.714307e+17 | Starting tomrrow we are giving away a free Mizu Life water bottle with every bag purchased in… https://t.co/T77G4PUsYW | 283395195 | 528 anacapa Santa Barbara | Point | c(34.42232, -119.70346) | en | 0 | 0 | 34.42232 | -119.7035 |
| Tue Jul 21 03:10:50 +0000 2015 | 6.233293e+17 | california, i’ll miss you @ Lucky Penny Santa Barbara https://t.co/QL5Q3i8Rfs | 783191605 | NA | Point | c(34.4145889, -119.6905289) | en | 0 | 0 | 34.41459 | -119.6905 |
| Sun Oct 16 21:35:22 +0000 2016 | 7.877689e+17 | Every Sunday you can shop the #StabilesMobile 🚚 on Canon Perdido + State St from 10-5pm go check… https://t.co/MEZqyobP4y | 1668713359 | Santa Barbara, California | Point | c(34.4204099, -119.70094) | en | 0 | 0 | 34.42041 | -119.7009 |
The spatial distribution of tweets highlights areas of higher population density and tourist areas in downtown Santa Barbara. There is a single coordinate that has over 11,000 tweets reported across all years. It is near De La Vina between Islay and Valerio. There is nothing remarkable about this site so I assume it is the default coordinate when people tag “Santa Barbara” generally. The coordinate is 34.4258, -119.714.
As you zoom in on the map, clusters will disaggregate. You can click on blue points to see the tweet.